Choosing K (elbow method, silhouette score) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the KMeans class from scikit-learn.

ML Python

from sklearn.cluster import [1]

Drag options to blanks, or click blank then click option'

ADecisionTreeClassifier

BLinearRegression

CPCA

DKMeans

Attempts:

3 left

2fill in blank

medium

Complete the code to fit a KMeans model with 3 clusters on data X.

ML Python

model = KMeans(n_clusters=[1])
model.fit(X)

Drag options to blanks, or click blank then click option'

A3

B1

C10

D5

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to compute the silhouette score for clustering labels.

ML Python

from sklearn.metrics import silhouette_score
score = silhouette_score(X, [1])

Drag options to blanks, or click blank then click option'

Amodel.fit(X)

Bmodel.predict(X)

Cmodel.labels_

DX

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a list of inertia values for K from 1 to 5.

ML Python

inertia_values = []
for k in range(1, 6):
    model = KMeans(n_clusters=[1])
    model.fit(X)
    inertia_values.append([2])

Drag options to blanks, or click blank then click option'

Ak

Bmodel.inertia_

Cmodel.score(X)

DX

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to compute silhouette scores for K from 2 to 6 and store them in a dictionary.

ML Python

silhouette_scores = {}
for k in range(2, 7):
    model = KMeans(n_clusters=[1])
    labels = model.fit_predict(X)
    silhouette_scores[[2]] = [3](X, labels)

Drag options to blanks, or click blank then click option'

Ak

Csilhouette_score

Dmodel.inertia_

Attempts:

3 left